text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from model.utils.config import cfg
  from model.faster_rcnn.faster_rcnn import _fasterRCNN
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  import math
  import torch.utils.model_zoo as model_zoo
  import pdb
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
         'resnet152']
  
  
  model_urls = {
    'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
    'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
    'resnet101': 'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth',
  }
  
  def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
             padding=1, bias=False)
  
  
  class BasicBlock(nn.Module):
    expansion = 1
  
    def __init__(self, inplanes, planes, stride=1, downsample=None):
      super(BasicBlock, self).__init__()
      self.conv1 = conv3x3(inplanes, planes, stride)
      self.bn1 = nn.BatchNorm2d(planes)
      self.relu = nn.ReLU(inplace=True)
      self.conv2 = conv3x3(planes, planes)
      self.bn2 = nn.BatchNorm2d(planes)
      self.downsample = downsample
      self.stride = stride
  
    def forward(self, x):
      residual = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
  
      if self.downsample is not None:
        residual = self.downsample(x)
  
      out += residual
      out = self.relu(out)
  
      return out
  
  
  class Bottleneck(nn.Module):
    expansion = 4
  
    def __init__(self, inplanes, planes, stride=1, downsample=None):
      super(Bottleneck, self).__init__()
      self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
                   padding=1, bias=False)
      self.bn2 = nn.BatchNorm2d(planes)
      self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
      self.bn3 = nn.BatchNorm2d(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
    def forward(self, x):
      residual = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
        residual = self.downsample(x)
  
      out += residual
      out = self.relu(out)
  
      return out
  
  
  class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
      self.inplanes = 64
      super(ResNet, self).__init__()
      self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                   bias=False)
      self.bn1 = nn.BatchNorm2d(64)
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True) # change
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
      self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
      self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
      # it is slightly better whereas slower to set stride = 1
      # self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
      self.avgpool = nn.AvgPool2d(7)
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      for m in self.modules():
        if isinstance(m, nn.Conv2d):
          n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
          m.weight.data.normal_(0, math.sqrt(2. / n))
        elif isinstance(m, nn.BatchNorm2d):
          m.weight.data.fill_(1)
          m.bias.data.zero_()
  
    def _make_layer(self, block, planes, blocks, stride=1):
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
        downsample = nn.Sequential(
          nn.Conv2d(self.inplanes, planes * block.expansion,
                kernel_size=1, stride=stride, bias=False),
          nn.BatchNorm2d(planes * block.expansion),
        )
  
      layers = []
      layers.append(block(self.inplanes, planes, stride, downsample))
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
        layers.append(block(self.inplanes, planes))
  
      return nn.Sequential(*layers)
  
    def forward(self, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
  
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      x = self.layer4(x)
  
      x = self.avgpool(x)
      x = x.view(x.size(0), -1)
      x = self.fc(x)
  
      return x
  
  
  def resnet18(pretrained=False):
    """Constructs a ResNet-18 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2])
    if pretrained:
      model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model
  
  
  def resnet34(pretrained=False):
    """Constructs a ResNet-34 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3])
    if pretrained:
      model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model
  
  
  def resnet50(pretrained=False):
    """Constructs a ResNet-50 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    if pretrained:
      model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model
  
  
  def resnet101(pretrained=False):
    """Constructs a ResNet-101 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3])
    if pretrained:
      model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model
  
  
  def resnet152(pretrained=False):
    """Constructs a ResNet-152 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3])
    if pretrained:
      model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model
  
  class resnet(_fasterRCNN):
    def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False):
      self.model_path = '/home/grad3/keisaito/data/pretrained_model/resnet101_caffe.pth'
      self.dout_base_model = 1024
      self.pretrained = pretrained
      self.class_agnostic = class_agnostic
  
      _fasterRCNN.__init__(self, classes, class_agnostic)
  
    def _init_modules(self):
      resnet = resnet101()
  
      if self.pretrained == True:
        print("Loading pretrained weights from %s" %(self.model_path))
        state_dict = torch.load(self.model_path)
        resnet.load_state_dict({k:v for k,v in state_dict.items() if k in resnet.state_dict()})
  
      # Build resnet.
      self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1,resnet.relu,
        resnet.maxpool,resnet.layer1,resnet.layer2,resnet.layer3)
  
      self.RCNN_top = nn.Sequential(resnet.layer4)
  
      self.RCNN_cls_score = nn.Linear(2048, self.n_classes)
      if self.class_agnostic:
        self.RCNN_bbox_pred = nn.Linear(2048, 4)
      else:
        self.RCNN_bbox_pred = nn.Linear(2048, 4 * self.n_classes)
  
      # Fix blocks
      for p in self.RCNN_base[0].parameters(): p.requires_grad=False
      for p in self.RCNN_base[1].parameters(): p.requires_grad=False
  
      assert (0 <= cfg.RESNET.FIXED_BLOCKS < 4)
      if cfg.RESNET.FIXED_BLOCKS >= 3:
        for p in self.RCNN_base[6].parameters(): p.requires_grad=False
      if cfg.RESNET.FIXED_BLOCKS >= 2:
        for p in self.RCNN_base[5].parameters(): p.requires_grad=False
      if cfg.RESNET.FIXED_BLOCKS >= 1:
        for p in self.RCNN_base[4].parameters(): p.requires_grad=False
  
      def set_bn_fix(m):
        classname = m.__class__.__name__
        if classname.find('BatchNorm') != -1:
          for p in m.parameters(): p.requires_grad=False
  
      self.RCNN_base.apply(set_bn_fix)
      self.RCNN_top.apply(set_bn_fix)
  
    def train(self, mode=True):
      # Override train so that the training mode is set as we want
      nn.Module.train(self, mode)
      if mode:
        # Set fixed blocks to be in eval mode
        self.RCNN_base.eval()
        self.RCNN_base[5].train()
        self.RCNN_base[6].train()
  
        def set_bn_eval(m):
          classname = m.__class__.__name__
          if classname.find('BatchNorm') != -1:
            m.eval()
  
        self.RCNN_base.apply(set_bn_eval)
        self.RCNN_top.apply(set_bn_eval)
  
    def _head_to_tail(self, pool5):
      fc7 = self.RCNN_top(pool5).mean(3).mean(2)
      return fc7
  